Artificial Neural Networks
Artificial Neural Networks are computational models inspired by an animal's central nervous systems (brain) that has the ability of machine learning. Artificial neural networks are generally presented as systems of interconnected "neurons" which can compute values from inputs (from wikipedia).
2. Training an Artificial Neural Network
The network is ready to be trained if it had been structured to service a particular application, meanwhile the initial weights are chosen randomly and after that the training begins.
There are two approaches in training Artificial Neural Networks: supervised and unsupervised.
2.1 Supervised Training
In supervised training, with a teacher, so we notice that both the inputs and the outputs are provided, compares its outputs result with the desired outputs. .
2.2 Unsupervised Training
The unsupervised training, without a teacher, so we see in the unsupervised training, the network is provided with inputs but without desired outputs. So system itself must then decide what features it will use to group or classify (clustering) the input data. This is often referred to as self-organization.
3. Some Issues In Neural networks
3.1Number of input nodes
Input sets are dynamic.Number of input is equal to number of features (columns), once we know the shape of our training data we can the input number ,some the methods like Sensitivity Based Pruning, Average Absolute Derivate Magnitude and others can be used to determine the input neuron numbers.
3.2Number of Output nodes
Output sets are dynamic. The number of output neurons is calculated by the chosen model configuration. The outpu...
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